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# neural-forecasts
Slackbot
05/04/2023, 2:24 PMThis message was deleted.
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Kin Gtz. Olivares
05/04/2023, 2:43 PMHey @Dawie van Lill,
Thanks for using NeuralForecast.
Here is the hyperparameter tuning documentation where we explain it: https://nixtla.github.io/neuralforecast/common.base_auto.html
Some ideas to be able to compare other methods:
1. If the models are not Pytorch-Lightning classes, you will need to write your own hyperparameter optimization functions, that homogeneize the inputs and outputs of the models and its evaluation and use directly a library like hyperopt.
2. If the models are Pytorch-Lightning classes you might be able to feed them into the
BaseAutoKin Gtz. Olivares
05/04/2023, 2:48 PM🔥
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Dawie van Lill
05/04/2023, 2:52 PMThe other models are ML based (SVR, RF, etc). We would have liked to use mlforecast, but it doesnt currently support the inclusion of historical and future exogenous variables as far as I can tell.
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Kin Gtz. Olivares
05/04/2023, 2:57 PMOne of the main advantages of NeuralForecast over MLForecast and alternatives is the automatic featurization, that avoids the hastle of feature engineering.
But in principle you should be able to feed the exogenous like in a classic XGB regression to model this problem:
Kin Gtz. Olivares
05/04/2023, 2:58 PMAs long as the rows of the forecast creation date match with your features, I think you should be able to use MLForecast.
Kin Gtz. Olivares
05/04/2023, 3:01 PMOn another note, the distinction between historic and future exogenous variables is fairly new innovation that very few forecasting models make. Which is another NeuralForecast advantage.
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Tyler Blume
05/05/2023, 2:31 PM@Kin Gtz. Olivares do you know if there are any plans to add more 'featurization' to MLForecast? There are some pretty basic things that can extend it and gives good performance boosts.
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Kin Gtz. Olivares
05/05/2023, 3:07 PMHey @Tyler Blume ,
If you have some ideas or feature requests add them here https://github.com/Nixtla/mlforecast/issues
We are happy to include them
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